Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

Documentation for package ‘bayesdfa’ version 1.3.4

bayesdfa-package	The 'bayesdfa' package.
bayesdfa	The 'bayesdfa' package.
dfa_cv	Apply cross validation to DFA model
dfa_fitted	Get the fitted values from a DFA as a data frame
dfa_loadings	Get the loadings from a DFA as a data frame
dfa_trends	Get the trends from a DFA as a data frame
find_dfa_trends	Find the best number of trends according to LOOIC
find_inverted_chains	Find which chains to invert
find_regimes	Fit multiple models with differing numbers of regimes to trend data
find_swans	Find outlying "black swan" jumps in trends
fit_dfa	Fit a Bayesian DFA
fit_regimes	Fit models with differing numbers of regimes to trend data
hmm_init	Create initial values for the HMM model.
invert_chains	Invert chains
is_converged	Summarize Rhat convergence statistics across parameters
loo	LOO information criteria
loo.bayesdfa	LOO information criteria
plot_fitted	Plot the fitted values from a DFA
plot_loadings	Plot the loadings from a DFA
plot_regime_model	Plot the state probabilities from 'find_regimes()'
plot_trends	Plot the trends from a DFA
predicted	Calculate predicted value from DFA object
rotate_trends	Rotate the trends from a DFA
sim_dfa	Simulate from a DFA
trend_cor	Estimate the correlation between a DFA trend and some other timeseries